{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "czech-musical",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rental-possible",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensorflow version:2.4.0\n"
     ]
    }
   ],
   "source": [
    "print(\"tensorflow version:{}\".format(tf.__version__))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "forty-worker",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "shaped-component",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"./222.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aboriginal-pulse",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>kfxs</th>\n",
       "      <th>mklz</th>\n",
       "      <th>csd</th>\n",
       "      <th>pjcd</th>\n",
       "      <th>zqd</th>\n",
       "      <th>dqzs</th>\n",
       "      <th>dlbqd</th>\n",
       "      <th>fsl</th>\n",
       "      <th>hd</th>\n",
       "      <th>mj</th>\n",
       "      <th>scdlqd</th>\n",
       "      <th>sjhs</th>\n",
       "      <th>xscvm</th>\n",
       "      <th>xscvb</th>\n",
       "      <th>xi40</th>\n",
       "      <th>xi50</th>\n",
       "      <th>cu50</th>\n",
       "      <th>xsmj140</th>\n",
       "      <th>xsmj200</th>\n",
       "      <th>xshz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>126.43</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.04</td>\n",
       "      <td>81.89</td>\n",
       "      <td>7.14</td>\n",
       "      <td>28.28</td>\n",
       "      <td>77.14</td>\n",
       "      <td>7.44</td>\n",
       "      <td>224.10</td>\n",
       "      <td>14.6</td>\n",
       "      <td>22.7</td>\n",
       "      <td>10.39</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>126.43</td>\n",
       "      <td>4.84</td>\n",
       "      <td>0.90</td>\n",
       "      <td>29.04</td>\n",
       "      <td>81.89</td>\n",
       "      <td>7.14</td>\n",
       "      <td>28.28</td>\n",
       "      <td>77.14</td>\n",
       "      <td>7.44</td>\n",
       "      <td>224.10</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.3</td>\n",
       "      <td>10.54</td>\n",
       "      <td>3.2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>42.3</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>125.64</td>\n",
       "      <td>4.64</td>\n",
       "      <td>0.89</td>\n",
       "      <td>29.63</td>\n",
       "      <td>81.60</td>\n",
       "      <td>7.13</td>\n",
       "      <td>27.28</td>\n",
       "      <td>77.85</td>\n",
       "      <td>7.34</td>\n",
       "      <td>253.00</td>\n",
       "      <td>14.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>10.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>124.21</td>\n",
       "      <td>4.67</td>\n",
       "      <td>0.89</td>\n",
       "      <td>29.60</td>\n",
       "      <td>81.36</td>\n",
       "      <td>7.17</td>\n",
       "      <td>27.31</td>\n",
       "      <td>77.64</td>\n",
       "      <td>7.32</td>\n",
       "      <td>251.14</td>\n",
       "      <td>14.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>10.34</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>25.8</td>\n",
       "      <td>6.3</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>128.10</td>\n",
       "      <td>4.58</td>\n",
       "      <td>0.89</td>\n",
       "      <td>30.12</td>\n",
       "      <td>81.68</td>\n",
       "      <td>7.24</td>\n",
       "      <td>27.54</td>\n",
       "      <td>77.57</td>\n",
       "      <td>7.31</td>\n",
       "      <td>219.00</td>\n",
       "      <td>15.7</td>\n",
       "      <td>20.8</td>\n",
       "      <td>10.54</td>\n",
       "      <td>1.4</td>\n",
       "      <td>4.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>5.3</td>\n",
       "      <td>64.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>920</th>\n",
       "      <td>148.70</td>\n",
       "      <td>4.33</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.16</td>\n",
       "      <td>82.91</td>\n",
       "      <td>9.40</td>\n",
       "      <td>32.58</td>\n",
       "      <td>76.75</td>\n",
       "      <td>8.48</td>\n",
       "      <td>247.70</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>149.60</td>\n",
       "      <td>4.32</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.20</td>\n",
       "      <td>82.92</td>\n",
       "      <td>9.39</td>\n",
       "      <td>32.81</td>\n",
       "      <td>76.76</td>\n",
       "      <td>8.43</td>\n",
       "      <td>247.13</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>150.03</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.23</td>\n",
       "      <td>82.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>32.86</td>\n",
       "      <td>76.85</td>\n",
       "      <td>8.43</td>\n",
       "      <td>245.10</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>923</th>\n",
       "      <td>150.03</td>\n",
       "      <td>4.31</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.23</td>\n",
       "      <td>82.95</td>\n",
       "      <td>9.36</td>\n",
       "      <td>32.86</td>\n",
       "      <td>76.85</td>\n",
       "      <td>8.43</td>\n",
       "      <td>245.10</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>924</th>\n",
       "      <td>150.93</td>\n",
       "      <td>4.30</td>\n",
       "      <td>0.93</td>\n",
       "      <td>29.27</td>\n",
       "      <td>82.96</td>\n",
       "      <td>9.34</td>\n",
       "      <td>33.09</td>\n",
       "      <td>76.86</td>\n",
       "      <td>8.38</td>\n",
       "      <td>244.53</td>\n",
       "      <td>12.9</td>\n",
       "      <td>18.5</td>\n",
       "      <td>12.34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>52.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>24.5</td>\n",
       "      <td>244.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>925 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd      mj  \\\n",
       "0    126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10   \n",
       "1    126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10   \n",
       "2    125.64  4.64  0.89  29.63  81.60  7.13  27.28  77.85  7.34  253.00   \n",
       "3    124.21  4.67  0.89  29.60  81.36  7.17  27.31  77.64  7.32  251.14   \n",
       "4    128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31  219.00   \n",
       "..      ...   ...   ...    ...    ...   ...    ...    ...   ...     ...   \n",
       "920  148.70  4.33  0.93  29.16  82.91  9.40  32.58  76.75  8.48  247.70   \n",
       "921  149.60  4.32  0.93  29.20  82.92  9.39  32.81  76.76  8.43  247.13   \n",
       "922  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43  245.10   \n",
       "923  150.03  4.31  0.93  29.23  82.95  9.36  32.86  76.85  8.43  245.10   \n",
       "924  150.93  4.30  0.93  29.27  82.96  9.34  33.09  76.86  8.38  244.53   \n",
       "\n",
       "     scdlqd  sjhs  xscvm  xscvb  xi40  xi50  cu50  xsmj140  xsmj200  xshz  \n",
       "0      14.6  22.7  10.39    3.2   8.8   0.5   2.0     23.0      4.5  5.86  \n",
       "1      15.0  22.3  10.54    3.2   8.8   0.3   1.8     42.3      6.8  5.87  \n",
       "2      14.8  20.5  10.34    2.5   5.0   0.3   2.5     25.8      6.3  5.34  \n",
       "3      14.8  20.5  10.34    2.5   5.0   0.3   2.5     25.8      6.3  5.34  \n",
       "4      15.7  20.8  10.54    1.4   4.8   0.3   5.3     64.0     13.0  5.79  \n",
       "..      ...   ...    ...    ...   ...   ...   ...      ...      ...   ...  \n",
       "920    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "921    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "922    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "923    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "924    12.9  18.5  12.34    2.0  52.5   0.5  24.5    244.0     59.0  5.42  \n",
       "\n",
       "[925 rows x 20 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "miniature-withdrawal",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "blocked-latest",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.iloc[:,10:]\n",
    "y = data.iloc[:,0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "liberal-reach",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输入10个维度，输出10个，六个隐藏层 激活函数sigmoid\n",
    "model = tf.keras.Sequential([tf.keras.layers.Dense(3,input_shape=(10,),activation='sigmoid'),\n",
    "                            tf.keras.layers.Dense(10)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "average-kazakhstan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 3)                 33        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 10)                40        \n",
      "=================================================================\n",
      "Total params: 73\n",
      "Trainable params: 73\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dynamic-payment",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer = 'adam',loss='mse')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "local-wallace",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8000\n",
      "29/29 [==============================] - 0s 1ms/step - loss: 7693.6081\n",
      "Epoch 2/8000\n",
      "29/29 [==============================] - 0s 798us/step - loss: 7600.3450\n",
      "Epoch 3/8000\n",
      "29/29 [==============================] - 0s 795us/step - loss: 7640.0649\n",
      "Epoch 4/8000\n",
      "29/29 [==============================] - 0s 865us/step - loss: 7619.2965\n",
      "Epoch 5/8000\n",
      "29/29 [==============================] - 0s 685us/step - loss: 7656.9638\n",
      "Epoch 6/8000\n",
      "29/29 [==============================] - 0s 702us/step - loss: 7666.3864\n",
      "Epoch 7/8000\n",
      "29/29 [==============================] - 0s 713us/step - loss: 7688.3987\n",
      "Epoch 8/8000\n",
      "29/29 [==============================] - 0s 719us/step - loss: 7590.4077\n",
      "Epoch 9/8000\n",
      "29/29 [==============================] - 0s 705us/step - loss: 7532.0897\n",
      "Epoch 10/8000\n",
      "29/29 [==============================] - 0s 728us/step - loss: 7622.0582\n",
      "Epoch 11/8000\n",
      "29/29 [==============================] - 0s 641us/step - loss: 7633.4875\n",
      "Epoch 12/8000\n",
      "29/29 [==============================] - 0s 643us/step - loss: 7470.9936\n",
      "Epoch 13/8000\n",
      "29/29 [==============================] - 0s 702us/step - loss: 7396.2804\n",
      "Epoch 14/8000\n",
      "29/29 [==============================] - 0s 740us/step - loss: 7479.1142\n",
      "Epoch 15/8000\n",
      "29/29 [==============================] - 0s 732us/step - loss: 7423.6222\n",
      "Epoch 16/8000\n",
      "29/29 [==============================] - 0s 666us/step - loss: 7394.6888\n",
      "Epoch 17/8000\n",
      "29/29 [==============================] - 0s 713us/step - loss: 7441.0752\n",
      "Epoch 18/8000\n",
      "29/29 [==============================] - 0s 749us/step - loss: 7420.9818\n",
      "Epoch 19/8000\n",
      "29/29 [==============================] - 0s 707us/step - loss: 7527.8250\n",
      "Epoch 20/8000\n",
      "29/29 [==============================] - 0s 696us/step - loss: 7439.7070\n",
      "Epoch 21/8000\n",
      "29/29 [==============================] - 0s 661us/step - loss: 7501.7107\n",
      "Epoch 22/8000\n",
      "29/29 [==============================] - 0s 663us/step - loss: 7401.8181\n",
      "Epoch 23/8000\n",
      "29/29 [==============================] - 0s 675us/step - loss: 7419.4385\n",
      "Epoch 24/8000\n",
      "29/29 [==============================] - 0s 673us/step - loss: 7295.8181\n",
      "Epoch 25/8000\n",
      "29/29 [==============================] - 0s 710us/step - loss: 7468.1550\n",
      "Epoch 26/8000\n",
      "29/29 [==============================] - 0s 690us/step - loss: 7380.1813\n",
      "Epoch 27/8000\n",
      "29/29 [==============================] - 0s 630us/step - loss: 7355.0868\n",
      "Epoch 28/8000\n",
      "29/29 [==============================] - 0s 593us/step - loss: 7307.1956\n",
      "Epoch 29/8000\n",
      "29/29 [==============================] - 0s 591us/step - loss: 7358.3256\n",
      "Epoch 30/8000\n",
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    },
    {
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     "text": [
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     "text": [
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     ]
    },
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     "text": [
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     ]
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    {
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     "text": [
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     ]
    },
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     "text": [
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    },
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    },
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    },
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    {
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     ]
    },
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     ]
    },
    {
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     "text": [
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     ]
    },
    {
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     ]
    },
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     "text": [
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    },
    {
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    },
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      "29/29 [==============================] - 0s 614us/step - loss: 428.2333\n",
      "Epoch 7991/8000\n",
      "29/29 [==============================] - 0s 605us/step - loss: 488.8325\n",
      "Epoch 7992/8000\n",
      "29/29 [==============================] - 0s 640us/step - loss: 357.9678\n",
      "Epoch 7993/8000\n",
      "29/29 [==============================] - 0s 635us/step - loss: 436.6822\n",
      "Epoch 7994/8000\n",
      "29/29 [==============================] - 0s 646us/step - loss: 384.2752\n",
      "Epoch 7995/8000\n",
      "29/29 [==============================] - 0s 668us/step - loss: 421.7189\n",
      "Epoch 7996/8000\n",
      "29/29 [==============================] - 0s 669us/step - loss: 375.2054\n",
      "Epoch 7997/8000\n",
      "29/29 [==============================] - 0s 595us/step - loss: 410.2097\n",
      "Epoch 7998/8000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29/29 [==============================] - 0s 635us/step - loss: 392.9650\n",
      "Epoch 7999/8000\n",
      "29/29 [==============================] - 0s 686us/step - loss: 403.6817\n",
      "Epoch 8000/8000\n",
      "29/29 [==============================] - 0s 669us/step - loss: 489.3897\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f04bdbd9240>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y,epochs = 8000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cross-generator",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = data.iloc[:10,10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "wound-cardiff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ],\n",
       "       [131.73163  ,   4.800926 ,   0.9115179,  28.65455  ,  82.75379  ,\n",
       "          7.7205987,  29.032644 ,  74.880615 ,   9.124584 , 205.06181  ]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "capital-teacher",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_data = data.iloc[:10,:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "geological-series",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     kfxs  mklz   csd   pjcd    zqd  dqzs  dlbqd    fsl    hd      mj\n",
      "0  126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10\n",
      "1  126.43  4.84  0.90  29.04  81.89  7.14  28.28  77.14  7.44  224.10\n",
      "2  125.64  4.64  0.89  29.63  81.60  7.13  27.28  77.85  7.34  253.00\n",
      "3  124.21  4.67  0.89  29.60  81.36  7.17  27.31  77.64  7.32  251.14\n",
      "4  128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31  219.00\n",
      "5  128.10  4.58  0.89  30.12  81.68  7.24  27.54  77.57  7.31  219.00\n",
      "6  125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50  201.33\n",
      "7  125.63  4.80  0.89  29.57  81.81  7.13  27.62  77.39  7.50  201.33\n",
      "8  127.32  4.80  0.90  29.38  81.47  7.24  28.85  77.73  7.41  213.27\n",
      "9  127.32  4.87  0.90  29.28  81.52  7.21  28.94  77.86  7.35  206.18\n"
     ]
    }
   ],
   "source": [
    "print(true_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "closing-keyboard",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "incident-leeds",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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